Improving translation of animal models of addiction and relapse by reverse translation
Critical features of human addiction are increasingly being incorporated into complementary animal models, including escalation of drug intake, punished drug seeking and taking, intermittent drug access, choice between drug and non-drug rewards, and assessment of individual differences based on crit...
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Published in | Nature reviews. Neuroscience Vol. 21; no. 11; pp. 625 - 643 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
01.11.2020
Nature Publishing Group |
Subjects | |
Online Access | Get full text |
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Summary: | Critical features of human addiction are increasingly being incorporated into complementary animal models, including escalation of drug intake, punished drug seeking and taking, intermittent drug access, choice between drug and non-drug rewards, and assessment of individual differences based on criteria in the fourth edition of the
Diagnostic and Statistical Manual of Mental Disorders
(DSM-IV). Combined with new technologies, these models advanced our understanding of brain mechanisms of drug self-administration and relapse, but these mechanistic gains have not led to improvements in addiction treatment. This problem is not unique to addiction neuroscience, but it is an increasing source of disappointment and calls to regroup. Here we first summarize behavioural and neurobiological results from the animal models mentioned above. We then propose a reverse translational approach, whose goal is to develop models that mimic successful treatments: opioid agonist maintenance, contingency management and the community-reinforcement approach. These reverse-translated ‘treatments’ may provide an ecologically relevant platform from which to discover new circuits, test new medications and improve translation.
Recent advances in animal addiction models have emphasized translational challenges. In this Review, Venniro and colleagues introduce a reverse translational approach that may provide an ecologically relevant platform from which to discover new circuits, test new medications and improve translation. |
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ISSN: | 1471-003X 1471-0048 1471-0048 1469-3178 |
DOI: | 10.1038/s41583-020-0378-z |